ReviewDigital biomarkers
The Digital Outcome Measure.
Review in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Article
- Ethical integration of patient-reported outcomes and digital biomarkers in AI healthcare models: an expert consensus framework.Frontiers in digital health · 2026Article
- Remote Patient Monitoring and Connected Technologies in Inflammatory Bowel Disease.Gastroenterology & hepatology · 2025Article
- Breast cancer: pathogenesis and treatments.Signal transduction and targeted therapy · 2025Review
- Article
- Aligning mission to digital health strategy in academic medical centers.NPJ digital medicine · 2022Review
- The digital transformation of hepatology: The patient is logged in.Hepatology (Baltimore, Md.) · 2022Review
- Using the MEAT VBP Framework to analyse and understand the value of surgical gloves: an explanatory case study.Health economics review · 2021Article
- Translational precision medicine: an industry perspective.Journal of translational medicine · 2021Review
- Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders.Journal of psychiatry & neuroscience : JPN · 2021Review
- Harnessing digital health to objectively assess cancer-related fatigue: The impact of fatigue on mobility performance.PloS one · 2021Article
- Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review.Journal of personalized medicine · 2020Article
- A systematic review of feasibility studies promoting the use of mobile technologies in clinical research.NPJ digital medicine · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Improving clinical outcomes remains the gold standard in advancing healthcare. Focusing on outcomes holds the potential to unite all clinical stakeholders including payers, industry, providers, and patients. Yet, the dominant ways in which outcomes are captured, provider-collected or patient-reported, have significant limitations. The emerging field of biosensors and wearables, which aims to capture many types of health data, holds promise to specifically capture outcomes while complementing existing outcome collection methods. A digital outcome measure, unlike a traditional provider-collected or patient-reported outcome measure, depends less on active patient or provider participation. Thus, digital outcome measures may be more amenable to standardization as well as greater collection consistency, frequency, and accuracy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.